scholarly journals Development of Deep Learning Algorithm using Convolutional Neural Network for Medical Imaging

2020 ◽  
Vol 9 (3) ◽  
pp. 139-142
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Brain Tumors ◽  
Learning Algorithm ◽  
Internal Organs ◽  
Clinical Images ◽  
Methodology Development ◽  
Deep Learning Algorithm ◽  
Graphical Illustration ◽  
Research Analysis

Medical imaging is the procedure and approach of formulating graphic models of the peculiarity of a body system for medical investigation and treatment, and also graphical illustration of the function of several internal organs or structures. To identify the affected tissues of the brain in a case of brain tumors, it is important to get high precision and accuracy to locate exact pixels. Manual analysis may be erroneous and so it is important to use deep learning image segmentation technique. Segmentation of graphic is the technique of dividing a graphic in to several group of pixels. The earlier objective of the segmentation is actually to produce details much easier and enhance the manifestation of clinical images into significant content. Segmentation is a complicated activity due to the excessive variability in the graphics. The computational intelligence is modern way for application automation. Existing studies shows need of deep learning research for fast and accurate medical imaging solutions. Hence, this paper presents the CNN framework (for an analysis of brain tumors) as a base for further research methodology development. The paper also provides a pilot research analysis that can further be used to develop improved precision and visibility

scholarly journals Development of Deep Learning Algorithm for Brain Tumor Segmentation

2019 ◽  
Vol 9 (1) ◽  
pp. 2800-2803
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Learning Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Human Errors ◽  
Deep Learning Algorithm ◽  
Proposed Model ◽  
A New Technique

Medical imaging is an emerging field in engineering. As traditional way of brain tumor analysis, MRI scanning is the way to identify brain tumor. The core drawback of manual MRI studies conducted by surgeons is getting manual visual errorswhich can lead toofa false identification of tumor boundaries. To avoid such human errors, ultra age engineering adopted deep learning as a new technique for brain tumor segmentation. Deep learning convolution network can be further developed by means of various deep learning models for better performance. Hence, we proposed a new deep learning algorithm development which can more efficiently identifies the types of brain tumors in terms of level of tumor like T1, T2, and T1ce etc. The proposed system can identify tumors using convolution neural network(CNN) which works with the proposed algorithm “Sculptor DeepCNet”. The proposed model can be used by surgeons to identify post-surgical remains (if any) of brain tumors and thus proposed research can be useful for ultra-age neural surgical image assessments. This paper discusses newly developed algorithm and its testing results.

scholarly journals Explainable COVID-19 Detection on Chest X-rays Using an End-to-End Deep Convolutional Neural Network Architecture

2021 ◽  
Vol 5 (4) ◽  
pp. 73
Author(s):  
Mohamed Chetoui ◽  
Moulay A. Akhloufi ◽  
Bardia Yousefi ◽  
El Mostafa Bouattane
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Binary Classification ◽  
X Rays ◽  
Deep Learning Algorithm ◽  
Binary Model ◽  
Chest X Ray ◽  
Multi Class Classification

The coronavirus pandemic is spreading around the world. Medical imaging modalities such as radiography play an important role in the fight against COVID-19. Deep learning (DL) techniques have been able to improve medical imaging tools and help radiologists to make clinical decisions for the diagnosis, monitoring and prognosis of different diseases. Computer-Aided Diagnostic (CAD) systems can improve work efficiency by precisely delineating infections in chest X-ray (CXR) images, thus facilitating subsequent quantification. CAD can also help automate the scanning process and reshape the workflow with minimal patient contact, providing the best protection for imaging technicians. The objective of this study is to develop a deep learning algorithm to detect COVID-19, pneumonia and normal cases on CXR images. We propose two classifications problems, (i) a binary classification to classify COVID-19 and normal cases and (ii) a multiclass classification for COVID-19, pneumonia and normal. Nine datasets and more than 3200 COVID-19 CXR images are used to assess the efficiency of the proposed technique. The model is trained on a subset of the National Institute of Health (NIH) dataset using swish activation, thus improving the training accuracy to detect COVID-19 and other pneumonia. The models are tested on eight merged datasets and on individual test sets in order to confirm the degree of generalization of the proposed algorithms. An explainability algorithm is also developed to visually show the location of the lung-infected areas detected by the model. Moreover, we provide a detailed analysis of the misclassified images. The obtained results achieve high performances with an Area Under Curve (AUC) of 0.97 for multi-class classification (COVID-19 vs. other pneumonia vs. normal) and 0.98 for the binary model (COVID-19 vs. normal). The average sensitivity and specificity are 0.97 and 0.98, respectively. The sensitivity of the COVID-19 class achieves 0.99. The results outperformed the comparable state-of-the-art models for the detection of COVID-19 on CXR images. The explainability model shows that our model is able to efficiently identify the signs of COVID-19.

scholarly journals Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm

2018 ◽  
Vol 138 (7) ◽  
pp. 1529-1538 ◽  
Author(s):  
Seung Seog Han ◽  
Myoung Shin Kim ◽  
Woohyung Lim ◽  
Gyeong Hun Park ◽  
Ilwoo Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Clinical Images ◽  
Deep Learning Algorithm

scholarly journals Analysis and Design of Deep Learning Algorithms for Retinal Image Classification for Early Detection of Diabetic Retinopathy

2021 ◽  
Vol 12 (6) ◽  
pp. 2633-2641
Author(s):  
Amita Meshram, Dr. Deepak Dembla, Dr. Reema Ajmera
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Retinal Image ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Clinical Images ◽  
Analysis And Design ◽  
Deep Learning Algorithm ◽  
Image Investigation ◽  
Clinical Determination

This paper presents a far reaching survey of the standard and use of deep learning in retinal image investigation. Many eye ailments regularly lead to visual impairment without legitimate clinical determination and clinical treatment. For instance, diabetic retinopathy (DR) is one such illness in which the retinal veins of natural eyes are harmed. The ophthalmologists analyze DR dependent on their expert information that is work escalated. With the advances in image preparing and man-made reasoning, Personal Computer vision-based methods have been applied quickly and broadly in the field of clinical images investigation. The important deep learning algorithms such as CNN Convolution Neural Network, ConvNet based algorithm, LCD net and Deep CNN, their working and main features of some of these standard  Deep Learning algorithm are analyzed in detailed. Proposed algorithm will become more reliable accurate by introducing new features as well as better quality input by using advance algorithm of image processing.  

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